{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Third Party Libraries With Rich Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A number of third party libraries defined their own custom display logic. This gives their objcts rich output by default when used in the Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pandas](http://pandas.pydata.org/) is a data analysis library for Python. Its `DataFrame` objects have an HTML table representation in the Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a small amount of stock data for APPL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing data.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile data.csv\n",
    "Date,Open,High,Low,Close,Volume,Adj Close\n",
    "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
    "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
    "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
    "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
    "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
    "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read this as into a `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pandas.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And view the HTML representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2012-06-01</td>\n",
       "      <td> 569.16</td>\n",
       "      <td> 590.00</td>\n",
       "      <td> 548.50</td>\n",
       "      <td> 584.00</td>\n",
       "      <td> 14077000</td>\n",
       "      <td> 581.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2012-05-01</td>\n",
       "      <td> 584.90</td>\n",
       "      <td> 596.76</td>\n",
       "      <td> 522.18</td>\n",
       "      <td> 577.73</td>\n",
       "      <td> 18827900</td>\n",
       "      <td> 575.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2012-04-02</td>\n",
       "      <td> 601.83</td>\n",
       "      <td> 644.00</td>\n",
       "      <td> 555.00</td>\n",
       "      <td> 583.98</td>\n",
       "      <td> 28759100</td>\n",
       "      <td> 581.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2012-03-01</td>\n",
       "      <td> 548.17</td>\n",
       "      <td> 621.45</td>\n",
       "      <td> 516.22</td>\n",
       "      <td> 599.55</td>\n",
       "      <td> 26486000</td>\n",
       "      <td> 596.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2012-02-01</td>\n",
       "      <td> 458.41</td>\n",
       "      <td> 547.61</td>\n",
       "      <td> 453.98</td>\n",
       "      <td> 542.44</td>\n",
       "      <td> 22001000</td>\n",
       "      <td> 540.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 2012-01-03</td>\n",
       "      <td> 409.40</td>\n",
       "      <td> 458.24</td>\n",
       "      <td> 409.00</td>\n",
       "      <td> 456.48</td>\n",
       "      <td> 12949100</td>\n",
       "      <td> 454.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low   Close    Volume  Adj Close\n",
       "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
       "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
       "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
       "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
       "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
       "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SymPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[SymPy](http://sympy.org/) is a symbolic computing library for Python. Its equation objects have LaTeX representations that are rendered in the Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sympy.interactive.printing import init_printing\n",
    "init_printing(use_latex='mathjax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import sympy as sym\n",
    "from sympy import *\n",
    "x, y, z = symbols(\"x y z\")\n",
    "k, m, n = symbols(\"k m n\", integer=True)\n",
    "f, g, h = map(Function, 'fgh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
      ],
      "text/plain": [
       "        ⅈ⋅x \n",
       "3⋅π    ℯ    \n",
       "─── + ──────\n",
       " 2     2    \n",
       "      x  + y"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
      ],
      "text/plain": [
       "x⋅sin(x) - 1   1\n",
       "──────────── + ─\n",
       "     x         x"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1/x + (x*sin(x) - 1)/x\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
      ],
      "text/plain": [
       "     2      4        \n",
       "    x    5⋅x     ⎛ 6⎞\n",
       "1 + ── + ──── + O⎝x ⎠\n",
       "    2     24         "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1/cos(x)).series(x, 0, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vincent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Vincent](https://vincent.readthedocs.io/en/latest/) is a visualization library that uses the [Vega](http://trifacta.github.io/vega/) visualization grammar to build [d3.js](http://d3js.org/) based visualizations in the Notebook and on http://nbviewer.ipython.org. `Visualization` objects in Vincetn have rich HTML and JavaSrcript representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import vincent\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas.io.data as web\n",
    "import datetime\n",
    "all_data = {}\n",
    "date_start = datetime.datetime(2010, 1, 1)\n",
    "date_end = datetime.datetime(2014, 1, 1)\n",
    "for ticker in ['AAPL', 'IBM', 'YHOO', 'MSFT']:\n",
    "    all_data[ticker] = web.DataReader(ticker, 'yahoo', date_start, date_end)\n",
    "price = pd.DataFrame({tic: data['Adj Close']\n",
    "                      for tic, data in all_data.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "           <script>\n",
       "               \n",
       "                function vct_load_lib(url, callback){\n",
       "                      if(typeof d3 !== 'undefined' &&\n",
       "                         url === 'http://d3js.org/d3.v3.min.js'){\n",
       "                        callback()\n",
       "                      }\n",
       "                      var s = document.createElement('script');\n",
       "                      s.src = url;\n",
       "                      s.async = true;\n",
       "                      s.onreadystatechange = s.onload = callback;\n",
       "                      s.onerror = function(){\n",
       "                        console.warn(\"failed to load library \" + url);\n",
       "                        };\n",
       "                      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "                };\n",
       "                var vincent_event = new CustomEvent(\n",
       "                  \"vincent_libs_loaded\",\n",
       "                  {bubbles: true, cancelable: true}\n",
       "                );\n",
       "                \n",
       "               function load_all_libs(){\n",
       "                  console.log('Loading Vincent libs...')\n",
       "                  vct_load_lib('http://d3js.org/d3.v3.min.js', function(){\n",
       "                  vct_load_lib('http://d3js.org/d3.geo.projection.v0.min.js', function(){\n",
       "                  vct_load_lib('http://wrobstory.github.io/d3-cloud/d3.layout.cloud.js', function(){\n",
       "                  vct_load_lib('http://wrobstory.github.io/vega/vega.v1.3.3.js', function(){\n",
       "                  window.dispatchEvent(vincent_event);\n",
       "                  });\n",
       "                  });\n",
       "                  });\n",
       "                  });\n",
       "               };\n",
       "               if(typeof define === \"function\" && define.amd){\n",
       "                    if (window['d3'] === undefined ||\n",
       "                        window['topojson'] === undefined){\n",
       "                        require.config(\n",
       "                            {paths: {\n",
       "                              d3: 'http://d3js.org/d3.v3.min',\n",
       "                              topojson: 'http://d3js.org/topojson.v1.min'\n",
       "                              }\n",
       "                            }\n",
       "                          );\n",
       "                        require([\"d3\"], function(d3){\n",
       "                            console.log('Loading Vincent from require.js...')\n",
       "                            window.d3 = d3;\n",
       "                            require([\"topojson\"], function(topojson){\n",
       "                                window.topojson = topojson;\n",
       "                                load_all_libs();\n",
       "                            });\n",
       "                        });\n",
       "                    } else {\n",
       "                        load_all_libs();\n",
       "                    };\n",
       "               }else{\n",
       "                    console.log('Require.js not found, loading manually...')\n",
       "                    load_all_libs();\n",
       "               };\n",
       "\n",
       "           </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vincent.initialize_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"vise33c59d182bd4093bad74a04ddf24501\"></div>\n",
       "<script>\n",
       "   ( function() {\n",
       "     var _do_plot = function() {\n",
       "       if (typeof vg === 'undefined') {\n",
       "         window.addEventListener('vincent_libs_loaded', _do_plot)\n",
       "         return;\n",
       "       }\n",
       "       vg.parse.spec({\"axes\": [{\"scale\": \"x\", \"title\": \"Date\", \"type\": \"x\"}, {\"scale\": \"y\", \"title\": \"Price\", \"type\": \"y\"}], \"data\": [{\"name\": \"table\", \"values\": [{\"col\": \"AAPL\", \"idx\": 1262592000000, \"val\": 28.84}, {\"col\": \"IBM\", \"idx\": 1262592000000, \"val\": 119.53}, {\"col\": \"YHOO\", \"idx\": 1262592000000, \"val\": 17.1}, {\"col\": \"MSFT\", \"idx\": 1262592000000, \"val\": 26.94}, {\"col\": \"AAPL\", \"idx\": 1262678400000, \"val\": 28.89}, {\"col\": \"IBM\", \"idx\": 1262678400000, \"val\": 118.09}, {\"col\": \"YHOO\", \"idx\": 1262678400000, \"val\": 17.23}, {\"col\": \"MSFT\", \"idx\": 1262678400000, \"val\": 26.95}, {\"col\": \"AAPL\", \"idx\": 1262764800000, \"val\": 28.43}, {\"col\": \"IBM\", \"idx\": 1262764800000, \"val\": 117.32}, {\"col\": \"YHOO\", \"idx\": 1262764800000, \"val\": 17.17}, {\"col\": \"MSFT\", \"idx\": 1262764800000, \"val\": 26.79}, {\"col\": \"AAPL\", \"idx\": 1262851200000, \"val\": 28.38}, {\"col\": \"IBM\", \"idx\": 1262851200000, \"val\": 116.92}, {\"col\": \"YHOO\", \"idx\": 1262851200000, \"val\": 16.7}, {\"col\": \"MSFT\", \"idx\": 1262851200000, \"val\": 26.51}, {\"col\": \"AAPL\", \"idx\": 1262937600000, \"val\": 28.56}, {\"col\": \"IBM\", \"idx\": 1262937600000, \"val\": 118.09}, {\"col\": \"YHOO\", \"idx\": 1262937600000, \"val\": 16.7}, {\"col\": \"MSFT\", \"idx\": 1262937600000, \"val\": 26.69}, {\"col\": \"AAPL\", \"idx\": 1263196800000, \"val\": 28.31}, {\"col\": \"IBM\", \"idx\": 1263196800000, \"val\": 116.85}, {\"col\": \"YHOO\", \"idx\": 1263196800000, \"val\": 16.74}, {\"col\": \"MSFT\", \"idx\": 1263196800000, \"val\": 26.35}, {\"col\": \"AAPL\", \"idx\": 1263283200000, \"val\": 27.99}, {\"col\": \"IBM\", \"idx\": 1263283200000, \"val\": 117.78}, {\"col\": \"YHOO\", \"idx\": 1263283200000, \"val\": 16.68}, {\"col\": \"MSFT\", \"idx\": 1263283200000, \"val\": 26.18}, {\"col\": \"AAPL\", \"idx\": 1263369600000, \"val\": 28.39}, {\"col\": \"IBM\", \"idx\": 1263369600000, \"val\": 117.53}, {\"col\": \"YHOO\", \"idx\": 1263369600000, \"val\": 16.9}, {\"col\": \"MSFT\", \"idx\": 1263369600000, \"val\": 26.42}, {\"col\": \"AAPL\", \"idx\": 1263456000000, \"val\": 28.22}, {\"col\": \"IBM\", \"idx\": 1263456000000, \"val\": 119.41}, {\"col\": \"YHOO\", \"idx\": 1263456000000, \"val\": 17.12}, {\"col\": \"MSFT\", \"idx\": 1263456000000, \"val\": 26.95}, {\"col\": \"AAPL\", \"idx\": 1263542400000, \"val\": 27.75}, {\"col\": \"IBM\", \"idx\": 1263542400000, \"val\": 118.93}, {\"col\": \"YHOO\", \"idx\": 1263542400000, \"val\": 16.82}, {\"col\": \"MSFT\", \"idx\": 1263542400000, \"val\": 26.86}, {\"col\": \"AAPL\", \"idx\": 1263888000000, \"val\": 28.98}, {\"col\": \"IBM\", \"idx\": 1263888000000, \"val\": 121.06}, {\"col\": \"YHOO\", \"idx\": 1263888000000, \"val\": 16.75}, {\"col\": \"MSFT\", \"idx\": 1263888000000, \"val\": 27.07}, {\"col\": \"AAPL\", \"idx\": 1263974400000, \"val\": 28.53}, {\"col\": \"IBM\", \"idx\": 1263974400000, \"val\": 117.55}, {\"col\": \"YHOO\", \"idx\": 1263974400000, \"val\": 16.38}, {\"col\": \"MSFT\", \"idx\": 1263974400000, \"val\": 26.63}, {\"col\": \"AAPL\", \"idx\": 1264060800000, \"val\": 28.04}, {\"col\": \"IBM\", \"idx\": 1264060800000, \"val\": 116.42}, {\"col\": \"YHOO\", \"idx\": 1264060800000, \"val\": 16.2}, {\"col\": \"MSFT\", \"idx\": 1264060800000, \"val\": 26.12}, {\"col\": \"AAPL\", \"idx\": 1264147200000, \"val\": 26.65}, {\"col\": \"IBM\", \"idx\": 1264147200000, \"val\": 113.26}, {\"col\": \"YHOO\", \"idx\": 1264147200000, \"val\": 15.88}, {\"col\": \"MSFT\", \"idx\": 1264147200000, \"val\": 25.21}, {\"col\": \"AAPL\", \"idx\": 1264406400000, \"val\": 27.36}, {\"col\": \"IBM\", \"idx\": 1264406400000, \"val\": 113.82}, {\"col\": \"YHOO\", \"idx\": 1264406400000, \"val\": 15.86}, {\"col\": \"MSFT\", \"idx\": 1264406400000, \"val\": 25.52}, {\"col\": \"AAPL\", \"idx\": 1264492800000, \"val\": 27.75}, {\"col\": \"IBM\", \"idx\": 1264492800000, \"val\": 113.49}, 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36.04}, {\"col\": \"AAPL\", \"idx\": 1388476800000, \"val\": 78.19}, {\"col\": \"IBM\", \"idx\": 1388476800000, \"val\": 181.85}, {\"col\": \"YHOO\", \"idx\": 1388476800000, \"val\": 40.44}, {\"col\": \"MSFT\", \"idx\": 1388476800000, \"val\": 36.16}]}], \"height\": 300, \"legends\": [{\"fill\": \"color\", \"offset\": 0, \"properties\": {}, \"title\": \"Ticker\"}], \"marks\": [{\"from\": {\"data\": \"table\", \"transform\": [{\"keys\": [\"data.col\"], \"type\": \"facet\"}]}, \"marks\": [{\"properties\": {\"enter\": {\"stroke\": {\"field\": \"data.col\", \"scale\": \"color\"}, \"strokeWidth\": {\"value\": 2}, \"x\": {\"field\": \"data.idx\", \"scale\": \"x\"}, \"y\": {\"field\": \"data.val\", \"scale\": \"y\"}}}, \"type\": \"line\"}], \"type\": \"group\"}], \"padding\": \"auto\", \"scales\": [{\"domain\": {\"data\": \"table\", \"field\": \"data.idx\"}, \"name\": \"x\", \"range\": \"width\", \"type\": \"time\"}, {\"domain\": {\"data\": \"table\", \"field\": \"data.val\"}, \"name\": \"y\", \"nice\": true, \"range\": \"height\"}, {\"domain\": {\"data\": \"table\", \"field\": \"data.col\"}, \"name\": \"color\", \"range\": \"category20\", \"type\": \"ordinal\"}], \"width\": 600}, function(chart) {\n",
       "         chart({el: \"#vise33c59d182bd4093bad74a04ddf24501\"}).update();\n",
       "       });\n",
       "     };\n",
       "     _do_plot();\n",
       "   })();\n",
       "</script>\n",
       "<style>.vega canvas {width: 100%;}</style>\n",
       "        "
      ],
      "text/plain": [
       "<vincent.charts.Line at 0x107badb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = vincent.Line(price[['AAPL', 'IBM', 'YHOO', 'MSFT']], width=600, height=300)\n",
    "line.axis_titles(x='Date', y='Price')\n",
    "line.legend(title='Ticker')\n",
    "display(line)"
   ]
  }
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